# Quantitative assessment of lung opacities from CT of pulmonary artery imaging data in COVID-19 patients: artificial intelligence versus radiologist

## TL;DR

This study compares AI and radiologists in measuring lung opacities in CT scans of early-stage COVID-19 patients, showing AI provides reliable results.

## Contribution

Demonstrates that AI trained on non-contrast CT scans works well with contrast-enhanced images for assessing lung opacities in COVID-19.

## Key findings

- AI showed strong correlation (r2=0.70) with radiologists' average estimates of lung opacities.
- Bland-Altman analysis revealed minimal bias between AI and radiologists, with no outliers.
- AI tools can complement radiologists in clinical workflows for timely decision-making.

## Abstract

Artificial intelligence (AI) deep learning algorithms trained on non-contrast CT scans effectively detect and quantify acute COVID-19 lung involvement. Our study explored whether radiological contrast affects the accuracy of AI-measured lung opacities, potentially impacting clinical decisions. We compared lung opacity measurements from AI software with visual assessments by radiologists using CT pulmonary angiography (CTPA) images of early-stage COVID-19 patients.

This prospective single-centre study included 18 COVID-19 patients who underwent CTPA due to suspected pulmonary embolism. Patient demographics, clinical data, and 30-day and 90-day mortality were recorded. AI tool (Pulmonary Density Plug-in, AI-Rad Companion Chest CT, SyngoVia; Siemens Healthineers, Forchheim, Germany) was used to estimate the quantity of opacities. Visual quantitative assessments were performed independently by 2 radiologists.

There was a positive correlation between radiologist estimations (r2 = 0.57) and between the AI data and the mean of the radiologists’ estimations (r2 = 0.70). Bland-Altman plot analysis showed a mean bias of +3.06% between radiologists and −1.32% between the mean radiologist vs AI, with no outliers outside 2×SD for respective comparison.

The AI protocol facilitated a quantitative assessment of lung opacities and showed a strong correlation with data obtained from 2 independent radiologists, demonstrating its potential as a complementary tool in clinical practice.

In assessing COVID-19 lung opacities in CTPA images, AI tools trained on non-contrast images, provide comparable results to visual assessments by radiologists.

The Pulmonary Density Plug-in enables quantitative analysis of lung opacities in COVID-19 patients using contrast-enhanced CT images, potentially streamlining clinical workflows and supporting timely decision-making.